Create an interactive report¶
- Choose a dataset! You can use any dataset for this task. Here is a list of some real life data:
- https://data.boston.gov/organization
- https://catalog.data.gov/dataset/demographic-statistics-by-zip-code-acfc9
- https://catalog.data.gov/dataset/crimes-2001-to-present-398a4
- https://catalog.data.gov/dataset/lottery-mega-millions-winning-numbers-beginning-2002
- https://catalog.data.gov/dataset/traffic-violations-56dda
- or from any other source
- Create 5 different plots using bokeh or plotly
When you finished with the notebook, then convert it to html!
jupyter-nbconvert --execute worksheet-interactive.ipynb
You can find further interactive tools on the pyviz site:
Note:
I had to use the jupyter-nbconvert --execute worksheet-interactive.ipynb --ExecutePreprocessor.timeout=180 command to prevent timeout due to long computations.
I've chosen Chicago city's "Crimes - 2001 to present" dataset.
In [1]:
import os
import random
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
In [2]:
data_dir = './/data//'
data_file = data_dir + os.listdir(data_dir)[-1]
In [3]:
crime_data = pd.read_csv(data_file)
In [4]:
crime_data.head()
Out[4]:
In [5]:
X = crime_data['Longitude']
Y = crime_data['Latitude']
# Clear NaN values
X = X[~np.isnan(X)]
Y = Y[~np.isnan(Y)]
print('There are {0}/{1} entries ({2:.3f}% of all entries) without coordinates.'.format(len(crime_data) - len(X),
len(crime_data),
(1 - len(X)/len(crime_data))*100))
1. Some basic maps using geoviews, datashader and geopandas¶
In [6]:
from functools import partial
import colorcet as cc
import datashader as ds
import datashader.transfer_functions as tf
from datashader.colors import colormap_select
import geoviews as gv
from geoviews import opts
import geopandas as gpd
import cartopy.crs as ccrs
from holoviews.operation.datashader import datashade
gv.extension('bokeh')
In [7]:
cmap = partial(colormap_select)
In [8]:
fs = 1
cvs = ds.Canvas(plot_width=fs*600, plot_height=fs*600)
agg = cvs.points(crime_data, 'Longitude', 'Latitude')
ds.transfer_functions.Image.border=0
img = tf.set_background(tf.shade(agg, cmap=cmap(cc.fire, 0.2), how='log'), 'black')
In [9]:
img
Out[9]:
The plot indicates, there is a faulty row (or rows) in the database where the coordinates are incorrect. We can easily filter these entries out.
In [10]:
# Delete incorrect entries from crime_data and update the original
# Cutoff latitude was aquired by plotting the data on a temporary
# scatter plot
cutoff_lat = 41.6
crime_coords = crime_data[crime_data['Latitude']>cutoff_lat][['Latitude', 'Longitude']]
In [11]:
# Determine correct scale of figure to create
# a figure with correct proportions
x_max = np.max(crime_coords['Longitude'])
x_min = np.min(crime_coords['Longitude'])
y_max = np.max(crime_coords['Latitude'])
y_min = np.min(crime_coords['Latitude'])
x_per_y = (x_max - x_min) / (y_max - y_min)
Visualization using geoviews, datashader and geopandas
In [12]:
districts = gpd.read_file('./data/chicago_districts.shp',
encoding='utf8')
districts['area_km2'] = districts['shape_area'] / 1000 / 1000
districts.head()
Out[12]:
In [13]:
# Load districts as Polygons and Locations as Points and Overlay them on map
polys = gv.Polygons(data=districts, vdims=['community', 'area_km2'], crs=ccrs.PlateCarree())
points = gv.Points(data=crime_coords, kdims=['Longitude', 'Latitude'], crs=ccrs.PlateCarree())
In [14]:
plot = gv.tile_sources.CartoDark()\
* datashade(points, expand=False, height=2000, width=2000,
cmap=cc.fire, normalization='eq_hist')\
* polys.opts(alpha=0.1, color='white', tools=['hover'])
plot.opts(width=int(1000*x_per_y), height=1000, bgcolor='black')
Out[14]: